Switzerland AI Strategy Report - AI Watch - European Commission

# Switzerland AI Strategy Report - AI Watch - European Commission

> ## Excerpt > Switzerland AI Strategy Report

From the lab to the market

The Working Group on AI highlights that Switzerland has good quality research and innovation in AI, although challenges are high.

In terms of scientific research in AI, existing activities and challenges on AI are presented in the SATW report on Artificial intelligence in science and research, prepared for the SERI. Switzerland can rely on the dynamic research environment of well-known and long-established research centres, such as the Swiss AI Lab IDSIA, the IDIAP Research Institute, the ETH Zürich Competence centres, and the Centre for Intelligent Systems at EPFL. In addition, private research initiatives and universities complement this research context. At present, the experts of the Working Group on AI suggested that the existing policy initiatives are providing the appropriate support and the Federal Government can avoid taking further policy measures. In this sense, the research capacity around AI receives support by existing policies as the Federal education, research and innovation policy 2021-2024, the Digitalisation action plan for education, research and innovation, open and competitive federal instruments, and the strategic planning of universities for 2021-2024 that identified digitalisation and AI as key priorities.

To enhance innovation in AI, the SATW report on Artificial intelligence in the industry and public administration, prepared for the SERI, presented a detailed overview of overarching challenges on AI in industry and public administration. The performance of Switzerland as for the amount and quality of AI patents, and the number of Swiss AI start-ups – reported by the Working Group on AI – reveal a strong and competitive position. As such, the Working Group concludes that the industry itself is addressing quite well the challenges of AI. However, besides self-regulation by the industry, the Working Group highlights numerous policy initiatives in priority areas such as media, mobility, healthcare, finance, agriculture and energy and climate.

Media and public

The Working Group highlights the need to govern the role of intermediaries due to the increasing use of AI in the media and to the challenges that it may bring along (e.g. fake news),. A governance report outlining concrete policy actions will be submitted to the Federal Council by the end of 2021. Other actions will tackle the monitoring of media developments and the use of AI in the media.

Autonomous mobility

The report on Autonomous mobility and artificial intelligence, prepared in 2019 for the SERI, presents governance efforts on autonomous mobility. In this respect, both the Federal Roads Office (FEDRO) and the Federal Office of Transport (FOT) are following-up the development on automated vehicles to promote data exchange (e.g. report on Provision and exchange of data for automated road driving), ensure data protection and revise the legislative framework (revision of the Road Traffic Act (SVG) and the Railways Act (EBG)).

Health care

AI is bringing many opportunities to the health system by means of data-driven medicine that can improve prevention, prediction and monitoring. The development of data-driven analytical techniques and the introduction of AI in the health care sector increase the need for data and privacy protection. Due to this, the Federal Office of Public Health (FOPH) monitors the impact of AI on medicine and healthcare also to include potential revisions to the existing legislation on the Human Research Act for data protection and privacy, and on the Federal Act on Medicinal Products and Medical Devices for the use of AI in the clinic process.

Finance

The use of AI is automating and accelerating labour-intensive processes in the financial industry too. Therefore, the need of a proper governance emerges as the use of AI in this sector expands. The Federal Department of Finance (FDF) monitors AI developments in the financial sector to fix emerging issues through proper regulatory reviews. Among others, it regulates the operational risks and it outlines the behavioural obligations to use AI methods in the financial sector.

Agriculture

In the context of agriculture, AI facilitates precision farming through image recognition and harvesting robots, among other cognitive computer technologies. The Federal Office for Agriculture (FOAG) monitors developments in agriculture on an ongoing basis. To this end, it has set up a Business Intelligence Competence Centre, which is active in the field digital data and predictive analyses. In addition, the Federal Department of Economic Affairs, Education and Research (EAER), and the FOAG launched a Charter on the digitisation of Swiss agriculture and the food industry in 2018. This Charter aims to nurture a shared awareness and promotes cooperation among relevant stakeholders.

Energy

The deployment of AI can enable significant efficiency gains in energy supply. It can support the development of renewable energies, provide energy savings and thus contribute to climate protection. Overall, it can simplify the existing complexity of energy supply operations. In this respect, the Swiss Federal Office of Energy (SFOE) monitors and tackles the AI challenges in the energy industry (see Section Error! Reference source not found. on Societal Challenges).

To foster innovations in the private sector, the creation of testbeds is recommended for cyber security and the energy sector. To increase the use of AI in cybersecurity, The National Cybersecurity Centre (NCSC), and the Federal Department of Defence, Civil Protection and Sport (DDPS), in cooperation with the Federal Department of Foreign Affairs (FDFA) and the EAER, are launching a study to evaluate the potential of a Swiss AI test centre in this field. In the energy sector, the Federal Office for Energy offers a Pilot and Demonstration Programme to promote the development and testing of new technologies, including AI-related projects.

In addition to the demonstrated tremendous potential of AI in the private sector, the use of AI is also an effective means to increase the quality and efficiency of services in the public administration. To this aim, the Federal Customs Administration (FCA), the Swiss Federal Statistical Office (FSO) and the State Secretariat for Migration (SEM), support various projects, e.g.:

• The development of a chatbot solution to reduce the costs of border crossing and the establishment of a data analytics projects to conduct risk analysis and controls in smuggling of goods. Both projects are part of the DaziT Programme, which aims to modernise and digitalise the Federal Customs Administration;
• The Arealstatistik Deep Learning – ADELE project is a deep learning application for land use and land cover classification managed by the FSO;
• The project on Automation of NOGA coding (NOGauto) proposes machine-learning methods to encode data already available at the FSO;
• The FSO project on Machine Learning – Sosi conducts data analyses on the social security system with machine-learning approaches;
• The project on Data validation with Machine Learning aims to extend and speed up data validation in the FSO by means of machine learning algorithms and at the same time to improve data quality;
• The SEM project Job algorithm for asylum seekers is a pilot test of a machine learning system to distribute asylum seekers among the cantons while optimising the labour market.

To foster similar types of projects, the Working Group on AI recommends that the federal administration encourages data exchanges and exploits the large data collections available in public administrations by means of AI-related technologies. To this purpose, a cross-administrative recording of processes and shared access to data between public departments should be envisaged. In addition, the creation of an AI competence network with a specific focus on technical aspects of the application of AI in the federal administration could facilitate the sharing of good practices.

Source : https://ai-watch.ec.europa.eu/countries/switzerland/switzerland-ai-strategy-report_en